Enterprise AI Strategy & Delivery Leadership

Enterprise AI for Decisions That Matter

Reliable AI starts with a shared understanding of your business — its products, processes, and knowledge. I help pharma, life sciences, manufacturing, and other high-stakes enterprises build that foundation, so AI becomes accurate, dependable, and ready to scale.

Discuss Your AI Initiative Explore How I Help

30-minute confidential intro. No generic sales pitch.

20+ Years in Enterprise Software, AI & Data 4M+ Enterprise Documents Unified in Global Pharma Former CTO — PoolParty (now Graphwise)
Reliable enterprise AI built on a trusted, shared understanding of enterprise knowledge

Sound Familiar?

Most enterprise AI initiatives don't stall because of the model. They stall because the organization lacks a shared understanding of its own knowledge — its products, processes, documents, and the language that connects them across systems.

Different Systems, Different Answers

Knowledge lives in disconnected systems that don't share a common understanding - so the same question returns different answers depending on where you ask.

AI You Can't Fully Trust

Confident but incorrect answers are unacceptable when decisions affect compliance, quality, operations, or patient outcomes.

Answers No One Can Verify

When AI can't show where an answer came from, no one can verify, audit, or stand behind it — and trust quietly erodes in regulated environments.

Initiatives That Stall After the Pilot

The demo impressed everyone. Then it met the reality of enterprise knowledge, governance, and scale — and the momentum quietly disappeared.

Slow, Hesitant Adoption

When people don't trust the answers, they don't change how they work — and the business value you expected never materializes.

No Way to Prove It's Working

Without a way to measure quality, teams debate whether the AI is good enough — so you can't justify the investment or know when it's ready to scale.

How I Help

Three focused engagements for leaders who need their AI initiatives to deliver real business outcomes — clarity, reliability, and results, not another slide deck.

01

AI Assessment & Rescue

Find out why your AI initiative is underdelivering — and get a clear, business-ready path forward.

Your AI is live, but the results disappoint — inconsistent answers, low trust, slow adoption, stalled momentum. Before you rebuild anything or write off the investment, you need an honest diagnosis of what's really going wrong.

What's included:

  • A clear read on why results and adoption fall short
  • Assessment of answer accuracy, consistency & trust
  • How well your enterprise knowledge is understood and connected
  • Whether quality is being measured — or simply debated
  • A prioritized roadmap tied to business impact

Outcome: A clear, honest diagnosis and a prioritized roadmap your leadership can act on — so you fix root causes, not symptoms.

Assess Your Situation
02

Enterprise Knowledge Foundation

Build the shared understanding that makes enterprise AI reliable, trusted, and ready to scale.

AI becomes reliable when it genuinely understands your business — your products, processes, documents, and the language that connects them across systems. I design that foundation backwards from the decisions your teams need to make, then put the right technology underneath it.

What's included:

  • Discovery of the business questions AI must answer
  • A shared model of your knowledge, terminology & relationships
  • Connecting fragmented systems into one trusted view
  • The right approach to keep answers accurate and grounded
  • A way to measure and improve answer quality over time
  • Supporting technology — semantic models, knowledge graphs, retrieval — chosen to fit, not to impress

Outcome: A reusable knowledge foundation your AI initiatives build on — reliable, scalable, and aligned to how your organization actually works.

Build Your Foundation
03

AI Delivery Leadership

Move your AI initiative from promising prototype to trusted production at scale.

You have a working prototype but no clear path to production — and the business is waiting for results. I provide hands-on leadership to bridge that gap, aligning the technical decisions, the team, and the stakeholders around a reliable outcome.

What's included:

  • Architecture leadership & ownership of the path to production
  • Confident decisions on vendors, tools & approach
  • Mentoring your team & overseeing delivery
  • Quality and governance so results stay trustworthy
  • Clear communication that keeps stakeholders aligned

Outcome: Your initiative moves from "promising demo" to a production system the business trusts — with clear ownership, quality gates, and measurable progress.

Discuss Delivery Needs

Case Study: From Fragmented Knowledge to Enterprise AI at Pharma Scale

The Challenge

A global pharmaceutical organization needed its people to get reliable, trustworthy answers from critical enterprise knowledge — through AI.

That knowledge was scattered across 4+ million documents in five disconnected enterprise systems, each with its own terminology, structure, and version of the truth.

Early AI attempts produced inconsistent, hard-to-trust answers — a serious problem in a regulated environment where every answer has to be accurate and traceable.

The Solution

We built a shared understanding of the organization's knowledge — the foundation that let AI find the right information and answer reliably. It brought together:

  • One connected view across five fragmented systems
  • A consistent model of terminology and relationships
  • Answers grounded in source documents, not guesswork
  • Continuous measurement and improvement of answer quality
  • Supporting technology — semantic models, knowledge graphs, and hybrid retrieval — under the hood

Every decision started from real business questions and how people actually work — not from technology for its own sake.

From fragmented sources to a shared knowledge foundation — and answers teams can trust. Click to enlarge

My Role

I led the teams responsible for making enterprise knowledge findable, trustworthy, and usable by AI — and kept the work anchored to business outcomes.

Across these teams, I was responsible for:

  • Leading the workstreams that made knowledge searchable and answerable by AI
  • Designing the shared knowledge foundation
  • Setting the strategy to keep answers accurate and reliable
  • Establishing how answer quality was measured and governed
  • Aligning technical teams, domain experts, and business stakeholders

Today, the platform serves thousands of users across the organization with AI-powered chat and search over one trusted knowledge foundation — and has become the backbone for further AI initiatives built on top of it.

Impact

4M+ Enterprise documents made AI-accessible
5 Systems Disconnected pharma systems unified into one trusted knowledge foundation
1,500+ Users served — backbone for further AI initiatives
€3M+ Estimated annual business impact from AI-driven knowledge access

Why Enterprise Teams Work With Me

Tomas Knap — Enterprise AI Consultant

I help enterprise leaders understand why their AI initiatives struggle — and what it takes to make them succeed. I bring together three things that rarely sit with one person: business understanding, enterprise architecture, and hands-on AI delivery leadership.

I'm Tomas Knap, and for more than 20 years I've worked in enterprise software, data, and AI. As former CTO at Semantic Web Company / PoolParty (now Graphwise), I led the strategy, architecture, and teams behind enterprise AI platforms for organizations in pharma, manufacturing, and other industries.

Much of that work came down to one thing: giving AI a reliable understanding of the business. I designed and led programs that unified millions of documents from disconnected systems into a single trusted foundation — powering AI search, chat, and knowledge discovery for thousands of users in regulated environments.

Today, I work with the leaders accountable for AI outcomes — the people who need their initiatives to earn trust, drive adoption, and deliver real value. I've built production AI in exactly the conditions they face, where fragmented knowledge, compliance pressure, and stakeholder complexity are the norm, not the exception.

Background 20+ years in enterprise software, data & AI · Former CTO at Semantic Web Company / PoolParty (now Graphwise)
Proof AI platforms serving 1,500+ enterprise users · 4M+ documents unified across global pharma knowledge systems
Specialization Reliable enterprise AI · Shared enterprise understanding · Trusted decision-making · AI delivery leadership
Industries Pharma · Life sciences · Manufacturing · Knowledge-intensive enterprises

Let's Discuss Your AI Initiative

Whether you're launching a new AI initiative, scaling a prototype to production, or trying to understand why an existing one isn't delivering — let's talk. A focused conversation about your situation, not a generic sales call.

Email

[email protected]

LinkedIn

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What to expect: A 30-minute focused conversation about your enterprise AI initiative. No sales pressure. If I can help, I'll tell you how. If I can't, I'll tell you that too.

Your message goes directly to me. I personally review every inquiry.

Prefer email? [email protected]

Frequently Asked Questions

Why do our AI initiatives stall after the pilot?

Usually because the AI doesn't truly understand the business beneath it. A demo on a few clean documents is very different from production across millions of documents, multiple systems, and inconsistent terminology. When that shared understanding is missing, answers become unreliable, trust drops, and momentum stalls. Building that foundation is what gets initiatives moving again.

Why does AI give different answers across our systems?

Because each system has its own data, terminology, and structure — and nothing connects them into a single, shared understanding. AI can only reflect what it's given, so disconnected sources produce inconsistent answers. Creating one trusted view across those systems is what makes answers consistent, no matter where the question starts.

Can you rescue an AI initiative that isn't delivering?

Yes — it's one of the most common reasons leaders call me. Many organizations have an AI system that impressed in demos but disappoints in production: wrong answers, low trust, slow adoption. I diagnose the real root causes — usually in how knowledge is understood, connected, and quality-checked — and give you a concrete, business-ready roadmap to turn it around.

Will you work with our internal teams?

Absolutely. I don't replace your team — I accelerate them. I work alongside your engineers, data scientists, and business stakeholders to make better decisions faster. Think of it as embedded leadership: I bring the enterprise AI experience, your team brings the domain knowledge and continuity.

Which industries do you work with?

I focus on knowledge-heavy, high-stakes industries where AI accuracy and trust matter most: pharma, life sciences, manufacturing, and other enterprises with large, complex document collections. The common thread is regulated or business-critical knowledge that AI has to handle reliably.

How do engagements typically start?

With a 30-minute conversation about your situation — no pitch, no pressure. If there's a fit, we scope a focused engagement (usually an assessment or a foundation sprint) with clear deliverables and a defined timeline. Most clients start with a 2–4 week assessment before committing to longer engagements.